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Guest Editorial Introduction to the Special Section on Scalability and Privacy in Social Networks
IEEE Transactions on Network Science and Engineering ( IF 6.6 ) Pub Date : 2020-06-04 , DOI: 10.1109/tnse.2019.2959674
Donghyun Kim , My T. Thai , R. N. Uma

The papers in this special section focus on scalability and privacy in online social network services. (OSNs) The growing popularity of OSNs and their emerging applications attracted much attention from both academia and industry during recent years. Due to their nature, social networks are considered as sources of Big Data containing large amounts of privacy-sensitive information. A social network is frequently abstracted using mathematical tools, especially graph models, which are usually very large. As a result, it is of great importance to continuously improve the performance. These papers aim to collect recent progresses on these two important subjects, which are frequently co-related, and promotes the discussions about them. We appreciate contributions to this special section and the valuable and extensive efforts of the reviewers. The topics of this special section cover efficient rumor blocking on social networking, privacy issue on mobile crowdsensing, a new inference attack against social network, a scalable data publication scheme with user privacy protection, and a new random matrix-based approach to publish online social network graph with privacy protection.

中文翻译:

客座社交网络可伸缩性和隐私特别部分的社论介绍

本节中的论文重点关注在线社交网络服务中的可伸缩性和隐私。(OSN)近年来,OSN的日益普及及其新兴应用引起了学术界和行业的广泛关注。由于其性质,社交网络被视为包含大量隐私敏感信息的大数据源。经常使用数学工具(尤其是图形模型)来抽象社交网络,而数学工具通常非常大。结果,持续改善性能非常重要。这些论文旨在收集在这两个经常密切相关的重要主题上的最新进展,并促进有关它们的讨论。我们感谢对这一特殊部分的贡献以及审稿人的宝贵和广泛的努力。
更新日期:2020-06-30
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